考虑可平移负荷的综合能源系统动态优化调度策略

综合能源系统因多能互补、协调优化等特性受到了广泛关注,但该系统的热电机组在运行时的调峰能力具有一定局限性。为降低综合能源系统的用能成本,提升系统的用能效率,改善其调峰能力,文中提出了一种考虑可平移负荷的综合能源系统动态优化调度策略。该策略以系统的整体运维成本最小化为目标,结合可平移负荷和相关算例构建仿真模型,并采用自适应混沌粒子群算法进行求解。结果表明在引入可平移负荷时,多能源微网能够较好地达到削峰填谷目的,并降低系统综合运行成本,实现节能减排效果。同时,文中将传统粒子群算法与自适应混沌粒子群算法作比较,证明了自适应混沌粒子群算法在精度与效率上都优于传统的粒子群算法。

Dynamic Optimal Scheduling Strategy for Integrated Energy Systems Considering Shiftable Loads

The integrated energy system has attracted wide attention from all walks of life because of its multi-energy complementation, coordination and optimization and other characteristics. However, when the thermal power unit in the system is running, its peak shaving ability has certain limitations. In order to reduce the energy cost of the integrated energy system, increase the energy efficiency of the system and improve its peak shaving capacity, this study proposes a dynamic optimal dispatch strategy for the integrated energy system considering the shiftable load. With the aim of minimizing the overall operation and maintenance cost of the system, a simulation model is built by combining the translational load and related examples, and the adaptive chaotic particle swarm optimization algorithm is used to solve the problem. The results show that when the shiftable load is introduced, the multi-energy microgrid can better achieve the purpose of peak shaving and valley filling, and reduce the overall operating cost of the system, and achieve the effect of energy saving and emission reduction. At the same time, this study compares the traditional particle swarm algorithm with the adaptive chaotic particle swarm algorithm and verifies that the adaptive chaotic particle swarm algorithm is superior to the traditional particle swarm algorithm in terms of accuracy and efficiency.